针对图像匹配实时性受限的问题以及在图像投影拼接中出现的重影的问题, 提出了一种改进尺度不变特征变换(SIFT)快速图像拼接和重影优化算法。该算法通过图像之间共享信息量的相似性进行特征点的区域划分, 利用SIFT算法对相似重合区域进行特征点的检测以及提取, 减少无用区域的算法运算时间; 在图像拼接的阶段, 通过特征点计算投影矩阵, 并进行粗投影, 再根据特征点所在区域密集程度, 通过最佳拟合变换对特征点密集区域进行二次投影拼接, 减少拼接图像出现重影的问题。实验结果表明, 该算法与传统的SIFT算法相比, 在特征点提取效率提高了大约58%。在图像拼接结果上, 通过客观评价指标进行比较提高大约10%。

This study aims to address the real-time limitations of image matching and the problem of ghosting in image projection stitching. Hence, a fast and improved scale invariant feature transform (SIFT) image stitching and ghosting optimization algorithm was proposed. First, feature points were classified based on the similarity of the shared information between the images, and then, the SIFT algorithm was used to detect and extract the feature points of similar coincident regions. This approach required the algorithm to spend less time on the useless regions. At the image stitching stage, the projection matrix was calculated by feature points, and rough projection was performed. Thereafter, according to the density of the area where the feature points were located, secondary projection splicing was performed on the dense feature points area by optimal fitting transformation to reduce the ghosting problem. Experiments are performed, and the results demonstrate that compared with the traditional SIFT algorithm, the efficiency of feature point extraction is improved by approximately 58%. Similarly, the comparison by an objective evaluation index show that image stitching improved by approximately 10%.